Context Microservice architecture is an architectural style in modern software systems, characterized by small, independent services called microservices. This architecture is ideal to facilitate rapid feature deployment. However, it presents a challenge for software engineers, who often lack a comprehensive architectural view due to the distributed nature and complex interdependencies of microservices. Objective This paper presents a Model Driven Architecture approach for MicroService Architecture Recovery called MiSAR. Building on previous work that defined a Platform Independent Metamodel, this study seeks to extend this metamodel, introduce a Platform Specific Metamodel, and establish mapping rules. The goal is to enable the semi-automatic recovery of architectural models for microservice systems. Methods An empirical study was conducted on nine microservice systems to define MiSAR’s artefacts and support semiautomatic recovery of architectural models. These artefacts are then implemented and used to semi-automatically recover the architectures of three systems. The effectiveness of MiSAR is evaluated based on metrics such as recall, precision, and F-measure, to assess the recovered models against actual architectures. We also compared the recovered architectural models with the ones documented by the developers. Results The study identified key requirements for the Platform Independent Metamodel to support comprehensive microservice architecture recovery, leading to an incremental extension of the MiSAR Platform Independent Metamodel. Mapping rules were established to effectively transform Platform Specific Models into Platform Independent ones. Furthermore, MiSAR was successfully implemented to recover architecture models. An evaluation using three systems demonstrated that MiSAR could recover architectural models with a high degree of completeness and correctness when compared with the actual architecture. Conclusion The MiSAR artefacts, including the extended Platform Independent Metamodel and mapping rules, effectively produce expressive architectural models of microservice systems. Systems confirmed MiSAR’s ability to semi-automatically recover accurate architectural models, providing a holistic view often missing in current software engineering practices.

Micro service architectures are rapidly establishing themselves in the software industry as a more efficient and effective substitute for monolithic applications. In a micro service architecture, the application is broken down into many small elements called micro services. These are managed in a distributed way and typically involve several development teams. In such an environment, an architectural model can get lost along the way, making it difficult to perform many downstream software engineering tasks, such as migration, audit, integration or impact analysis. To address this problem, we are developing support for Micro Service Architecture Recovery (MiSAR) using a Model Driven Engineering approach. In this paper, we describe an empirical study which aims to identify the core elements of our approach, by undertaking manual analysis on 8 micro service-based open source projects. From this analysis, we define a metamodel for micro service-based architectures and a set of mapping rules which map between the software and the metamodel. The resulting metamodel and mapping rules provide a solid foundation for any micro service architecture recovery approach and hence are a key first step towards managing the architectural integrity of micro service-based applications.

The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.

A long time ago now (maybe 1988?), Gerald (Gazdar) and I supervised Adam's DPhil at the University of Sussex. Adam was my age, give or take a year, having come to academia a little late, and was my first doctoral student. Adam's topic was polysemy, and I'm not really sure that much supervision was actually required, though I recall fun exchanges trying to model the subtleties of word meaning using symbolic knowledge representation techniques—an experience that was clearly enough to convince Adam later that this was a bad idea. In fact, Adam's thesis title itself was Polysemy. Much as we encourage short thesis titles, pulling off the one-word title is a tall order, requiring a unique combination of focus and coverage, breadth and depth, and, most of all, authority. Adam completely nailed it, at least from the perspective of the pre-empirical Computational Linguistics of the early 1990s.Three years later, after a spell working for dictionary publishers, Adam joined me as a research fellow, now at the University of Brighton. I had a project to explore the automatic enrichment of lexical databases to support the latest trends in language analysis, and, in particular, task-specific lexical resources. I was really pleased and excited to recruit Adam—he had lost none of his intellectual independence, a quality I particularly valued. Within a few weeks he came to me with his own plan for the research—a “detour,” as he put it, from the original workplan. I still have the e-mail, dated 6 April 1995, in which he proposed that, instead of chasing a prescriptive notion of a single lexical resource that needed to be customized to each domain, we should let the domain determine the lexicon, providing lexicographic tools to explore words, and particularly word senses, that were significant for that domain. In that e-mail, Computational Lexicography at Brighton was born.Over the next eight years or so, Computational Lexicography became a key part of our group's success, increasingly under Adam's direct leadership. The key project, WASPS, developed the WASPbench—the direct precursor of the Sketch Engine, recruiting David (Tugwell) to the team. In addition, Adam was one of the founding organizers of SENSEVAL, an initiative to bring international teams of researchers together to work in friendly competition on a pre-determined word sense disambiguation task (and which has now transformed into SEMEVAL). Together we secured funding to support the first two rounds of SENSEVAL; each round required the preparation of standardized data sets, guided by Adam's highly tuned intuitions about lexical data preparation and management. And we engaged somewhat in the European funding merry-go-round, most fondly in the CONCEDE project, working on dictionaries for Central European languages with amazing teams from the MULTEXT-EAST consortium, and with Georgian and German colleagues in the GREG project.But Adam was not entirely comfortable in academia, or at least not in a version of academia that didn't share his drive for the practical as well as the theoretical. He didn't have tenure, nor any clear route to achieve tenure, which meant that he could not apply for and hold grants in his own right (although he freely admitted he was happy not to have the associated administrative responsibilities); he set up a high quality masters program in Computational Lexicography, which ran for a couple of years, but the funding model didn't really work, and it quickly evolved into the highly successful, but independent, Lexicom workshop series, still running today; and he couldn't engage university support for developing the WASPbench as a commercial product. So in 2003, he spread his wings, left the university, and set up Lexical Computing Ltd.For many people, Lexical Computing and the Sketch Engine are what Adam is best known for. He spent eleven years tirelessly developing the company, the software, the methodology, the resources, the discipline. It was an environment in which he seemed completely at ease, sometimes the shameless promoter of his wares, sometimes the astute academic authority, often the source of practical solutions to real problems, and the instigator of new initiatives, and always the generous facilitator, educator, and friend. For me personally, though, this was a time when our friendship was more prominent than our professional relationship. We would meet for the odd drink, usually in the Constant Service pub (Adam's favorite), and chat about life, family, sometimes work, and occasional schemes for new collaborations, though the company didn't leave him very much time for that. It was one of those relaxed, undemanding friendships that just picks up whenever and wherever we find the time to meet, but remains strong nevertheless.Adam's illness was as unexpected to him as to anyone. Over the summer of 2014, he was making plans for new directions and projects. And then, there was a brief hiatus in communication before we heard the news in early November. And yet, even then, he seemed reconciled—not resigned, but resolved, calm, dignified. I was upset, angry, helpless—useless really, and feeling very selfish in my distress. I saw Adam three times after he became ill and they are all good, strong memories, and that is more to his credit than mine.The first was in his kitchen, with early spring sunshine, drinking strong coffee he had made very meticulously, watching the winter birds scavenging in the garden, just chatting about nothing in particular, and gossiping about work for a couple of hours. The second was a surprise trip to the pub—the surprise being that Adam was strong enough to get there (and back) on his own, and drink a couple of pints, too. We went to the Constant Service, as always, and it was one of our occasional “NLP group” outings, so a good crowd was there. The third was back in his kitchen, this time for work a few weeks later. Ironically, the university system that struggled to engage with Adam's practical drive is now fully signed up to demonstrating the “impact” of its research. Adam's work on Computational Lexicography at Brighton and afterwards through Lexical Computing, featured as an “Impact Case Study” in recent national evaluations, has subsequently been selected for a wider national initiative showcasing UK Computer Science research, currently in development1. Adam was happy to cooperate with this, in part to alleviate boredom, and we arranged a Skype call with a technical author for the initiative from his kitchen. Adam was in excellent form describing his work, his passion, and still full of ideas for gentle academic engagement if his “retirement” would allow it.Shortly after that meeting we heard the news of Adam's relapse and decision not to continue treatment. Like everyone else, I followed his blog, and also emailed a little privately. I arranged to go and visit again, but Adam wasn't well enough so we cancelled. Like everyone else, I waited for the inevitable blog post.Adam's funeral was in a modest church in the village of Rottingdean just along the coast from Brighton. A beautiful setting and a sunny afternoon. The church was absolutely packed—standing room only—we estimate about 250 people; family, friends, and colleagues from far and wide. A committed atheist, the service focused on fond memories of Adam from those closest to him, with just one hymn, Immortal Invisible, as all his blog readers will understand. A beautiful and fitting farewell to a man who, it seems, was to everyone a friend first, and a colleague, boss, or antagonist, second.There have been many comments on Adam's blog, on twitter, in academic forums, which say much more and so much better than I can. Some have said that Adam will be remembered for the Sketch Engine and the amazing data resources that have been built up around it. I would say that his real legacy is much more deeply intellectual than that. Adam would probably smile with satisfaction that the two things can coexist so comfortably—a rare combination of the intellectual and practitioner, a real giant of the field.Rest now in peace, Adam.RogerBrighton, June 2015

The Deep Film Access Project (DFAP) aims to unlock latent opportunities that exist within the big and complex data sets generated by industrial digital film production. The project is developing a methodology for the integration of the data and metadata generated through film production, by capturing, archiving and making accessible the diverse range and levels of expertise which exist within filmmaking. As a proof-of-principle, the project uses the entire corpus of Sally Potter's 2012 film, Ginger & Rosa, providing an emblematic example of an industrial digital feature film production in contemporary Britain which features the work of a number of renowned and prolific practitioners in the UK and international film industry. Our initial focus is on a case-study of materials associated with a single scene from the film.

Abstract This paper describes CLIME, a web-based legal advisory system with a multilingual natural language interface. CLIME is a ‘proof-of-concept’ system which answers queries relating to ship-building and ship-operating regulations. Its core knowledge source is a set of such regulations encoded as a conceptual domain model and a set of formalised legal inference rules. The system supports retrieval of regulations via the conceptual model, and assessment of the legality of a situation or activity on a ship according to the legal inference rules. The focus of this paper is on the natural language aspects of the system, which help the user to construct semantically complex queries using WYSIWYM technology, allow the system to produce extended and cohesive responses and explanations, and support the whole interaction through a hybrid synchronous/asynchronous dialogue structure. Multilinguality (English and French) is viewed simply as interface localisation: the core representations are language-neutral, and the system can present extended or local interactions in either language at any time. The development of CLIME featured a high degree of client involvement, and the specification, implementation and evaluation of natural language components in this context are also discussed.

We present the RAGS (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.

In this paper we present a new approach to controlling the behaviour of a natural language generation system by correlating internal decisions taken during free generation of a wide range of texts with the surface stylistic characteristics of the resulting outputs, and using the correlation to control the generator. This contrasts with the generate-and-test architecture adopted by most previous empirically-based generation approaches, offering a more efficient, generic and holistic method of generator control. We illustrate the approach by describing a system in which stylistic variation (in the sense of Biber (1988)) can be effectively controlled during the generation of short medical information texts.

Generic software architectures aim to support re-use of components, focusing of research and development effort, and evaluation and comparison of approaches. In the field of natural language processing, generic frameworks for understanding have been successfully deployed to meet all of these aims, but nothing comparable yet exists for generation. The nature of the task itself, and the current methodologies available to research it, seem to make it more difficult to reach the necessary level of consensus to support generic proposals. Recent work has made progress towards establishing a generic framework for generation at the functional level, but left open the issue of actual implementation. In this paper, we discuss the requirements for such an implementation layer for generation systems, drawing on two initial attempts to implement it. We argue that it is possible and useful to distinguish “functional architecture” from “implementation architecture” for generation systems.

We present the RAGS (Reference Architecture for Generation Systems) framework, a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.

The RAGS proposals for generic specification of NLG systems includes a detailed account of data representation, but only an outline view of processing aspects. In this paper we introduce a modular processing architecture with a concrete implementation which aims to meet the RAGS goals of transparency and reusability. We illustrate the model with the RICHES system -- a generation system built from simple linguistically-motivated modules.

The RAGS project aims to define a reference architecture for Natural Language Generation (NLG) systems. Currently the major part of this architecture consists of a set of datatype definitions for specifying the input and output formats for modules within NLG systems. In this paper we describe our efforts to reinterpret an existing NLG system in terms of these definitions. The system chosen was the Caption Generation System.

This paper shows how DATR, a widely used formal language for lexical knowledge representation, can be used to define an I_TAG lexicon as an inheritance hierarchy with internal lexical rules.A bottom-up featural encoding is used for LTAG trees and this allows lexical rules to be implemented as covariation constraints within feature structures.Such an approach eliminates the considerable redundancy otherwise associated with an LTAG lexicon.

Abstract The Portable Extendable Traffic Information Collator (POETIC) is an information extraction system that extracts traffic information from free text occurring in police incident logs and initiates (simulated) broadcasts of traffic bulletins to motorists when appropriate. POETIC is a second stage prototype system; the initial prototype (TIC, see Evans and Hartley 1990) was limited to the practices and requirements of a single police force. In POETIC, the architecture and data representations have been generalised to make the system tailorable to many different police force ‘domains’. In this paper we describe these developments, and report on tests of the system on authentic input data from three police domains.

A highly flexible programmable IIR filter chip has been designed and fabricated to commercial requirements within a collaborative project involving several industrial partners. The device uses 8 highly regular 16 bit array multiplier-accumulators which have been pipelined to achieve an overall computational rate of 30 MHz using a 1 micron gate array process. Most significant bit first arithmetic has been employed to achieve the target 15 MHz sample rate whilst implementing an 8th order filter. The paper reviews the principles behind the filter chip and its architecture, and describes a modular system which has been built to facilitate its demonstration and evaluation.>